A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining
A filter feature selection method based on the Maximal Information Coefficient and Gram-Schmidt Orthogonalization for biomedical data mining
复制标题
一种基于最大信息系数和Gram-Schmidt正交化的生物医学数据挖掘过滤特征选择方法
DOI：
10.1016/j.compbiomed.2017.08.021
复制
发表时间：
2017-10-01
影响因子：
7.7
通讯作者：
Wang, Cheng
中科院分区：
文献类型：
--
作者：
Lyu, Hongqiang;Wan, Mingxi;Wang, Cheng
过滤特征选择技术已被广泛应用于生物医学数据挖掘。最近，在经典的过滤方法最小冗余最大相关性（mRMR），风险已被揭示，一个特定的部分的冗余，称为不相关的冗余，可能涉及在最小冗余组件的这种方法。因此，一些尝试，以消除不相关的冗余附加额外的程序mRMR，如核典型相关分析的mRMR（KCCAmRMR），已作出。针对这一问题，提出了一种基于最大信息系数（MIC）和Gram-Schmidt归一化（GSO）的滤波器特征选择方法--正交MIC特征选择（OMICFS）。与其他基于最大相关度和最小冗余度准则的改进方法不同，该方法利用最小相关度来量化特征变量与目标变量之间的相关程度，利用广义统计量来计算候选特征相对于先前选择的特征的正交化变量，最大相关度和最小冗余度可以通过最大化GSO正交变量与目标之间的MIC相关度来间接优化。这种正交化策略允许OMICFS排除不相关的冗余，而无需任何额外的过程。为了验证性能，OMICFS与其他过滤器特征选择方法进行了比较，在两种类型的生物医学数据集进行分类实验的分类精度和计算效率。结果表明，OMICFS在大多数情况下优于其他方法。此外，分析了这些方法之间的差异，并讨论了OMICFS在高维生物医学数据挖掘中的应用。所提出的方法的Matlab代码可在https://github.com/ Ihqxinghun/bioinformatics/tree/master/OMICFS/上获得。
A filter feature selection technique has been widely used to mine biomedical data. Recently, in the classical filter method minimal-Redundancy-Maximal-Relevance (mRMR), a risk has been revealed that a specific part of the redundancy, called irrelevant redundancy, may be involved in the minimal-redundancy component of this method. Thus, a few attempts to eliminate the irrelevant redundancy by attaching additional procedures to mRMR, such as Kernel Canonical Correlation Analysis based mRMR (KCCAmRMR), have been made. In the present study, a novel filter feature selection method based on the Maximal Information Coefficient (MIC) and Gram-Schmidt Orthogonalization (GSO), named Orthogonal MIC Feature Selection (OMICFS), was proposed to solve this problem. Different from other improved approaches under the max-relevance and min-redundancy criterion, in the proposed method, the MIC is used to quantify the degree of relevance between feature variables and target variable, the GSO is devoted to calculating the orthogonalized variable of a candidate feature with respect to previously selected features, and the max-relevance and min-redundancy can be indirectly optimized by maximizing the MIC relevance between the GSO orthogonalized variable and target. This orthogonalization strategy allows OMICFS to exclude the irrelevant redundancy without any additional procedures. To verify the performance, OMICFS was compared with other filter feature selection methods in terms of both classification accuracy and computational efficiency by conducting classification experiments on two types of biomedical datasets. The results showed that OMICFS outperforms the other methods in most cases. In addition, differences between these methods were analyzed, and the application of OMICFS in the mining of high-dimensional biomedical data was discussed. The Matlab code for the proposed method is available at https://github.com/ Ihqxinghun/bioinformatics/tree/master/OMICFS/.